The Impact of Artificial Intelligence as a General-Purpose Technology on Economic Growth and Structural Transformation: An Innovation Ecosystem Perspective

Sultan Salur Kucuk · Economies · 25 Jun 2026

Where this sits in the debate

Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

A structured review of 2015–2025 evidence framing AI as a general-purpose technology working through three channels—TFP, task reallocation, and innovation dynamics—and concluding that growth effects are neither uniform nor automatic: outcomes hinge on complementary conditions like human capital, data infrastructure, and institutional governance capacity rather than the technology itself. This conditional, ecosystem-centered reading helps explain why similar AI investments produce divergent macro outcomes across countries, a key corrective to deterministic AI-growth forecasts.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

Through which channels does AI, viewed as a general-purpose technology, affect economic growth and structural change?

The approach

A structured, mechanism-oriented literature review of work from 2015 to 2025, using transparent search, screening and thematic classification rather than a formal meta-analysis, organised into an innovation ecosystem framework.

The findings

The review links three channels: total factor productivity, task reallocation and labour-market restructuring, and innovation dynamics. It reports that growth effects are not uniform, estimates vary across contexts, and outcomes depend mostly on complementary conditions such as human capital, data infrastructure and governance capacity.

How to read it: review. A synthesis of existing studies — breadth over novelty; quality depends on what it includes and excludes.

Limitations and counterpoints AI-written

It is a narrative synthesis without meta-analytic pooling, so it does not produce a single estimate of AI's growth contribution.

Abstract

This article examines how artificial intelligence (AI), conceptualized as a general-purpose technology (GPT), shapes economic growth and structural transformation through a structured literature review covering the period from 2015 to 2025. The study adopts a structured, mechanism-oriented synthesis approach grounded in transparent search, screening, and thematic classification procedures rather than formal meta-analytic protocols. It develops an integrative innovation ecosystem framework that links three core transmission channels: (i) total factor productivity (TFP), (ii) task reallocation and labor-market restructuring, and (iii) innovation and knowledge-generation dynamics. The findings indicate that AI adoption does not generate uniform or automatic growth effects. Empirical evidence remains heterogeneous, and estimates of AI’s macroeconomic contribution vary across institutional and structural contexts. In most cases, outcomes depend less on the technology itself and more on complementary conditions—human capital formation, digital and data infrastructure, institutional coordination, and governance capacity—that enable effective diffusion. Interpreting task-based automation models alongside endogenous-growth and open-innovation frameworks clarifies why similar AI investments may lead to divergent structural outcomes. Rather than proposing a deterministic growth model, the study advances a conditional and ecosystem-centered interpretation of AI-led development. The study contributes by distinguishing foundational theoretical perspectives from the contemporary 2015–2025 evidence base, clarifying the relationship between task transformation and structural transformation, and emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes.

About this publication

Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.

Published: 2026 (4 months ago)
Citations: 0 (OpenAlex)